Below tests run on 01/07/21 with github code pull on 01/06/21:
# make file paths relative so they run on other machines
setwd(here())
source("helper_testthat.R")
# setwd("~/Box Sync/jlee/Maya/metasens_website/Main site/tests_human_inspection")
# source("helper_testthat.R")
# source("~/Box Sync/jlee/Maya/evalue/EValue/tests/helper_testthat.R")
# source("~/Box Sync/jlee/Maya/evalue/EValue/R/meta-analysis.R")
# setwd("~/Box Sync/jlee/Maya/evalue/tests_human_inspection/")
test1 gbc_prepped.csv file - correct
d = read.csv("Datasets for website test/gbc_prepped.csv", stringsAsFactors = FALSE)
# note: log(0.9) = -0.1053605
confounded_meta(method="calibrated",
q = log(.9),
r = 0.1,
muB = log(1),
tail = "below",
yi.name = "yi",
vi.name = "vi",
dat = d,
R = 500)
## [1] "All values of t are equal to 1 \n Cannot calculate confidence intervals"
## Prop is already less than or equal to r even with no confounding, so Tmin and Gmin are simply equal to 1. No confounding at all is required to make the specified shift.
## Value Est SE CI.lo CI.hi
## 1 Prop 0.02358491 0.01459543 0 0.08056641
## 2 Tmin 1.00000000 0.00000000 1 NA
## 3 Gmin 1.00000000 0.00000000 1 NA
### R output:
# [1] "All values of t are equal to 1 \n Cannot calculate confidence intervals"
# Prop is already less than or equal to r even with no confounding, so Tmin and Gmin are simply equal to 1. No confounding at all is required to make the specified shift.
# Value Est SE CI.lo CI.hi
# 1 Prop 0.02358491 0.01553758 0 0.08018868
# 2 Tmin 1.00000000 0.00000000 1 NA
# 3 Gmin 1.00000000 0.00000000 1 NA
# Warning message:
# In norm.inter(t, adj.alpha) : extreme order statistics used as endpoints
sens_plot(method="calibrated",
type = "line",
q = log(0.9),
tail = "below",
Bmin = log(1),
Bmax = log(4),
yi.name = "yi",
vi.name = "vi",
dat = d,
R = 500)
## Warning: Problem with `mutate()` input `..1`.
## ℹ extreme order statistics used as endpoints
## ℹ Input `..1` is `...[]`.
## ℹ The error occurred in row 28.
## Warning in norm.inter(t, adj.alpha): extreme order statistics used as endpoints
## Warning: Problem with `mutate()` input `..1`.
## ℹ extreme order statistics used as endpoints
## ℹ Input `..1` is `...[]`.
## ℹ The error occurred in row 35.
## Warning in norm.inter(t, adj.alpha): extreme order statistics used as endpoints
## Warning: Problem with `mutate()` input `..1`.
## ℹ extreme order statistics used as endpoints
## ℹ Input `..1` is `...[]`.
## ℹ The error occurred in row 36.
## Warning in norm.inter(t, adj.alpha): extreme order statistics used as endpoints
## Some of the pointwise confidence intervals were not estimable via bias-corrected and accelerated bootstrapping, so the confidence band on the plot may not be shown for some values of the bias factor. This usually happens at values with a proportion estimate close to 0 or 1. You can try increasing the number of bootstrap iterates or choosing a less extreme threshold.
### Website output:
knitr::include_graphics("mytest-expected/001.png")
test2 gbc_prepped.csv file - correct
d = read.csv("Datasets for website test/gbc_prepped.csv")
# note: log(0.5) = -0.6931472
confounded_meta(method="calibrated",
q = log(0.5),
r = 0.5,
muB = log(0.5),
tail = "above",
yi.name = "yi",
vi.name = "vi",
dat = d,
R = 500)
## Error in confounded_meta(method = "calibrated", q = log(0.5), r = 0.5, : Must have muB > 0. Use the muB.toward.null argument instead if you want to consider bias away from the null. See Details.
### R output:
# Error in confounded_meta(method = "calibrated", q = log(0.5), r = 0.5, :
# Must have muB > 0. Use the muB.toward.null argument instead if you want to consider bias away from the null. See Details.
sens_plot(method="calibrated",
type = "line",
q = log(.5),
tail = "above",
Bmin = log(1),
Bmax = log(6),
yi.name = "yi",
vi.name = "vi",
dat = d,
R = 500)
## [1] "All values of t are equal to 1 \n Cannot calculate confidence intervals"
## Warning: Problem with `mutate()` input `..1`.
## ℹ extreme order statistics used as endpoints
## ℹ Input `..1` is `...[]`.
## ℹ The error occurred in row 2.
## Warning in norm.inter(t, adj.alpha): extreme order statistics used as endpoints
## Warning: Problem with `mutate()` input `..1`.
## ℹ extreme order statistics used as endpoints
## ℹ Input `..1` is `...[]`.
## ℹ The error occurred in row 3.
## Warning in norm.inter(t, adj.alpha): extreme order statistics used as endpoints
## Warning: Problem with `mutate()` input `..1`.
## ℹ extreme order statistics used as endpoints
## ℹ Input `..1` is `...[]`.
## ℹ The error occurred in row 4.
## Warning in norm.inter(t, adj.alpha): extreme order statistics used as endpoints
## Warning: Problem with `mutate()` input `..1`.
## ℹ extreme order statistics used as endpoints
## ℹ Input `..1` is `...[]`.
## ℹ The error occurred in row 5.
## Warning in norm.inter(t, adj.alpha): extreme order statistics used as endpoints
## Warning: Problem with `mutate()` input `..1`.
## ℹ extreme order statistics used as endpoints
## ℹ Input `..1` is `...[]`.
## ℹ The error occurred in row 8.
## Warning in norm.inter(t, adj.alpha): extreme order statistics used as endpoints
## Some of the pointwise confidence intervals were not estimable via bias-corrected and accelerated bootstrapping, so the confidence band on the plot may not be shown for some values of the bias factor. This usually happens at values with a proportion estimate close to 0 or 1. You can try increasing the number of bootstrap iterates or choosing a less extreme threshold.
### R output:
# Some of the pointwise confidence intervals were not estimable via bias-corrected and accelerated bootstrapping, so the confidence band on the plot may not be shown for some values of the bias factor. This usually happens at values with a proportion estimate close to 0 or 1. Otherwise, you can try increasing R.
# Warning messages:
# 1: In norm.inter(t, adj.alpha) :
# extreme order statistics used as endpoints
# 2: In norm.inter(t, adj.alpha) :
# extreme order statistics used as endpoints
# 3: In norm.inter(t, adj.alpha) :
# extreme order statistics used as endpoints
# 4: In norm.inter(t, adj.alpha) :
# extreme order statistics used as endpoints
### Website output (passing on log scale):
knitr::include_graphics("mytest-expected/002.png")
### Website output (passing on RR scale):
knitr::include_graphics("mytest-expected/003.png")
test3 gbc_prepped.csv file - correct
d = read.csv("Datasets for website test/gbc_prepped.csv")
## get error for column name
confounded_meta(method="calibrated",
q = log(.5),
r = 0.5,
muB = log(1.5),
tail = "above",
yi.name = "yi",
vi.name = "vyi",
dat = d,
R = 2000)
## Error in Phat_causal(q = q, B = muB, tail = tail, muB.toward.null = muB.toward.null, : dat does not contain a column named vi.name
### R output:
# Error in Phat_causal(q = q, B = muB, tail = tail, muB.toward.null = muB.toward.null, :
# dat does not contain a column named vi.name
### Website output:
knitr::include_graphics("mytest-expected/004.png")
test4 flegal_prepped.csv file - correct
d = read.csv("Datasets for website test/flegal_prepped.csv")
## on log-RR scale:
# note: log(0.5) = -0.6931472
confounded_meta(method= "calibrated",
q = -0.6931472,
r = 0.5,
muB = 0.5,
tail = "above",
yi.name = "yi",
vi.name = "vi",
dat = d,
R = 500)
## [1] "All values of t are equal to 1 \n Cannot calculate confidence intervals"
## The confidence interval and/or standard error for the proportion were not estimable via bias-corrected and accelerated bootstrapping. You can try increasing the number of bootstrap iterates or choosing a less extreme threshold.
## Value Est SE CI.lo CI.hi
## 1 Prop 1.000000 0.00000000 NA NA
## 2 Tmin 1.834157 0.02989419 1.786808 1.895639
## 3 Gmin 3.071078 0.06210765 2.972504 3.198639
### R output:
# [1] "All values of t are equal to 1 \n Cannot calculate confidence intervals"
# The confidence interval and/or standard error for the proportion were not estimable via bias-corrected and accelerated bootstrapping. You can try increasing the number of bootstrap iterates or choosing a less extreme threshold.
# Value Est SE CI.lo CI.hi
# 1 Prop 1.000000 0.00000000 NA NA
# 2 Tmin 1.834157 0.03195267 1.783244 1.905911
# 3 Gmin 3.071078 0.06635321 2.965072 3.219907
sens_plot(method= "calibrated",
type = "line",
q = -0.6931472,
tail = "above",
Bmin = 0,
Bmax = 1.38629436111989,
yi.name = "yi",
vi.name = "vi",
dat = d,
R = 500)
## [1] "All values of t are equal to 1 \n Cannot calculate confidence intervals"
## None of the pointwise confidence intervals was estimable via bias-corrected and accelerated bootstrapping, so the confidence band on the plot is omitted. You can try increasing the number of bootstrap iterates or choosing a less extreme threshold.
### R output:
# [1] "All values of t are equal to 1 \n Cannot calculate confidence intervals"
# None of the pointwise confidence intervals were not estimable via bias-corrected and accelerated bootstrapping, so the confidence band on the plot is omitted. You can try increasing R.
### Website output:
# pass input on log scale
knitr::include_graphics("mytest-expected/005.png")
test5 flegal_prepped.csv file - correct
d = read.csv("Datasets for website test/flegal_prepped.csv")
# note: log(0.5) = -0.6931472
# log(1.5) = 0.4054651
confounded_meta(method="calibrated",
q = log(.5),
r = 0.1,
muB = log(1.5),
tail = "above",
yi.name = "yi",
vi.name = "vi",
dat = d,
R = 500)
## [1] "All values of t are equal to 1 \n Cannot calculate confidence intervals"
## The confidence interval and/or standard error for the proportion were not estimable via bias-corrected and accelerated bootstrapping. You can try increasing the number of bootstrap iterates or choosing a less extreme threshold.
## Value Est SE CI.lo CI.hi
## 1 Prop 1.000000 0.00000000 NA NA
## 2 Tmin 2.177725 0.08806265 2.058396 2.350757
## 3 Gmin 3.779212 0.18013471 3.534404 4.132695
### R output:
# [1] "All values of t are equal to 1 \n Cannot calculate confidence intervals"
# The confidence interval and/or standard error for the proportion were not estimable via bias-corrected and accelerated bootstrapping. You can try increasing the number of bootstrap iterates or choosing a less extreme threshold.
# Value Est SE CI.lo CI.hi
# 1 Prop 1.000000 0.00000000 NA NA
# 2 Tmin 2.177725 0.08883057 2.061940 2.353965
# 3 Gmin 3.779212 0.18173756 3.541689 4.139234
### Website output:
# pass on log scale
knitr::include_graphics("mytest-expected/006.png")
# pass on RR scale
knitr::include_graphics("mytest-expected/007.png")
test6 flegal_prepped.csv file - correct
d = read.csv("Datasets for website test/flegal_prepped.csv")
confounded_meta(method="calibrated",
q = log(1.2),
r = 1.0,
muB = log(1),
tail = "above",
yi.name = "yi",
vi.name = "vi",
dat = d,
R = 500)
## [1] "All values of t are equal to 1 \n Cannot calculate confidence intervals"
## Prop is already less than or equal to r even with no confounding, so Tmin and Gmin are simply equal to 1. No confounding at all is required to make the specified shift.
## Value Est SE CI.lo CI.hi
## 1 Prop 0.03571429 0.01643293 0.007142857 0.07142857
## 2 Tmin 1.00000000 0.00000000 1.000000000 NA
## 3 Gmin 1.00000000 0.00000000 1.000000000 NA
### R output:
# [1] "All values of t are equal to 1 \n Cannot calculate confidence intervals"
# Prop is already less than or equal to r even with no confounding, so Tmin and Gmin are simply equal to 1. No confounding at all is required to make the specified shift.
# Value Est SE CI.lo CI.hi
# 1 Prop 0.03571429 0.01584981 0.007142857 0.06428571
# 2 Tmin 1.00000000 0.00000000 1.000000000 NA
# 3 Gmin 1.00000000 0.00000000 1.000000000 NA
### Website output:
knitr::include_graphics("mytest-expected/008.png")
test7 data_calib_test_1-1.csv file - correct
d = read.csv("Datasets for website test/data_calib_test_1-1.csv")
## an example with crazy input
confounded_meta(method="calibrated",
q = log(0),
r = 0,
muB = log(1),
tail = "above",
yi.name = "est",
vi.name = "var",
dat = d,
R = 0)
## [1] "All values of t are equal to NaN \n Cannot calculate confidence intervals"
## The confidence interval and/or standard error for the proportion were not estimable via bias-corrected and accelerated bootstrapping. You can try increasing the number of bootstrap iterates or choosing a less extreme threshold.
## [1] "All values of t are equal to NaN \n Cannot calculate confidence intervals"
## The confidence interval and/or standard error for Tmin and Gmin were not estimable via bias-corrected and accelerated bootstrapping. You can try increasing the number of bootstrap iterates or choosing a less extreme threshold.
## Value Est SE CI.lo CI.hi
## 1 Prop 1 NA NA NA
## 2 Tmin Inf NA NA NA
## 3 Gmin NaN NA NA NA
### R output:
# [1] "All values of t are equal to NaN \n Cannot calculate confidence intervals"
# The confidence interval and/or standard error for the proportion were not estimable via bias-corrected and accelerated bootstrapping. You can try increasing the number of bootstrap iterates or choosing a less extreme threshold.
# [1] "All values of t are equal to NaN \n Cannot calculate confidence intervals"
# The confidence interval and/or standard error for Tmin and Gmin were not estimable via bias-corrected and accelerated bootstrapping. You can try increasing the number of bootstrap iterates or choosing a less extreme threshold.
# Value Est SE CI.lo CI.hi
# 1 Prop 1 NA NA NA
# 2 Tmin Inf NA NA NA
# 3 Gmin NaN NA NA NA
sens_plot(method="calibrated",
type = "line",
q = log(0),
tail = "above",
Bmin = log(0),
Bmax = log(0),
yi.name = "yi",
vi.name = "vi",
dat = d,
R = 0)
## Error in seq.default(Bmin, Bmax, 0.01): 'from' must be a finite number
### R output:
# Error in seq.default(Bmin, Bmax, 0.01) : 'from' must be a finite number
### Website output:
knitr::include_graphics("mytest-expected/009.png")
test8 - correct
## parametric method test
confounded_meta(method="parametric",
q=log(1.1),
r=0.2,
tail="above",
muB=log(1.2),
sigB=sqrt(0.35*0.1),
yr=log(1.2),
vyr=0.01,
t2=0.1,
vt2=0.01)
## Value Est SE CI.lo CI.hi
## 1 Prop 0.3542627 0.1809323 0.000000 0.7088834
## 2 Tmin 1.4235523 0.2369612 1.000000 1.8879878
## 3 Gmin 2.2000501 0.5187985 1.183224 3.2168766
### R output:
# Value Est SE CI.lo CI.hi
# 1 Prop 0.3542627 0.1809323 0.000000 0.7088834
# 2 Tmin 1.4235523 0.2369612 1.000000 1.8879878
# 3 Gmin 2.2000501 0.5187985 1.183224 3.2168766
sens_plot(method = "parametric",
type="line",
q=log(1.1),
yr=log(1.2),
vyr=0.01,
t2=0.1,
vt2=0.01,
Bmin=log(1),
Bmax=log(4),
sigB=sqrt(0.35*0.1),
tail="above" )
## Warning in sens_plot(method = "parametric", type = "line", q = log(1.1), :
## Calculating parametric confidence intervals in the plot. For values of the
## proportion that are less than 0.15 or greater than 0.85, these confidence
## intervals may not perform well.
### R output:
# Warning message:
# In sens_plot(method = "parametric", type = "line", q = log(1.1), :
# Calculating parametric confidence intervals in the plot. For values of Phat that are less than 0.15 or greater than 0.85, these confidence intervals may not perform well.
### Website output:
# passing input on RR scale
knitr::include_graphics("mytest-expected/010.png")
# passing input on log-RR scale
knitr::include_graphics("mytest-expected/011.png")
test9 - correct
## parametric method test
## see what errors if all 0
confounded_meta(method="parametric",
q=log(0),
r=0,
tail="above",
muB=log(0),
sigB=0,
yr=log(0),
vyr=0,
t2=0,
vt2=0)
## Error in confounded_meta(method = "parametric", q = log(0), r = 0, tail = "above", : Must have muB > 0. Use the muB.toward.null argument instead if you want to consider bias away from the null. See Details.
### R output:
# Error in confounded_meta(method = "parametric", q = log(0), r = 0, tail = "above", :
# Must have muB > 0. Use the muB.toward.null argument instead if you want to consider bias away from the null. See Details.
### Website output:
knitr::include_graphics("mytest-expected/012.png")
test10 - correct
# note:
# log(0.5) = -0.6931472
# log(1.5) = 0.4054651
confounded_meta(method="parametric",
q=log(.5),
r=0.75,
tail="below",
muB=log(1.5),
sigB=sqrt(0.5*0.25),
yr=log(1.5),
vyr=0.5,
t2=0.25,
vt2=0.5)
## Warning in confounded_meta(method = "parametric", q = log(0.5), r = 0.75, : Prop
## is close to 0 or 1. We recommend choosing method = "calibrated" or alternatively
## using bias-corrected and accelerated bootstrapping to estimate all inference in
## this case.
## Prop is already less than or equal to r even with no confounding, so Tmin and Gmin are simply equal to 1. No confounding at all is required to make the specified shift.
## Value Est SE CI.lo CI.hi
## 1 Prop 0.02496774 0.00057182 0.02384699 0.02608848
## 2 Tmin 1.00000000 NA NA NA
## 3 Gmin 1.00000000 NA NA NA
### R output:
# Prop is already less than or equal to r even with no confounding, so Tmin and Gmin are simply equal to 1. No confounding at all is required to make the specified shift.
# Value Est SE CI.lo CI.hi
# 1 Prop 0.02496774 0.00057182 0.02384699 0.02608848
# 2 Tmin 1.00000000 NA NA NA
# 3 Gmin 1.00000000 NA NA NA
# Warning message:
# In confounded_meta(method = "parametric", q = log(0.5), r = 0.75, :
# Prop is close to 0 or 1. We recommend choosing method = "calibrated" or alternatively using bias-corrected and accelerated bootstrapping to estimate all inference in this case.
sens_plot(method = "parametric",
type="line",
q=log(.5),
yr=log(1.5),
vyr=0.5,
t2=0.25,
vt2=sqrt(0.5*(0.25)),
Bmin=log(1),
Bmax=log(4),
sigB=sqrt(0.5*0.25),
tail="below" )
## Warning in sens_plot(method = "parametric", type = "line", q = log(0.5), :
## Calculating parametric confidence intervals in the plot. For values of the
## proportion that are less than 0.15 or greater than 0.85, these confidence
## intervals may not perform well.
### R output:
# Warning message:
# In sens_plot(method = "parametric", type = "line", q = log(0.5), :
# Calculating parametric confidence intervals in the plot. For values of Phat that are less than 0.15 or greater than 0.85, these confidence intervals may not perform well.
### Website output:
knitr::include_graphics("mytest-expected/013.png")
test11 - correct
## on log-RR scale
# log(1.2)
## parametric method test
confounded_meta(method="parametric",
q=0.1823216,
r=0.2,
tail="below",
muB=.2,
sigB=sqrt(0.15*0.25),
yr=.4,
vyr=0.05,
t2=0.25,
vt2=0.05)
## Value Est SE CI.lo CI.hi
## 1 Prop 0.4847044 0.1799780 0.131954 0.8374548
## 2 Tmin 1.2252345 0.3580879 1.000000 1.9270738
## 3 Gmin 1.7505582 0.8524453 1.000000 3.4213202
### R output:
# Value Est SE CI.lo CI.hi
# 1 Prop 0.4847044 0.1935404 0.1053722 0.8640366
# 2 Tmin 1.2252345 0.5534300 1.0000000 2.3099373
# 3 Gmin 1.7505582 1.3174664 1.0000000 4.3327449
sens_plot(method = "parametric",
type="line",
q=0.1823216,
yr=.4,
vyr=0.05,
t2=0.25,
vt2=0.05,
Bmin=0,
Bmax=1.791759,
sigB=sqrt(0.15*0.25),
tail="below" )
## Warning in sens_plot(method = "parametric", type = "line", q = 0.1823216, :
## Calculating parametric confidence intervals in the plot. For values of the
## proportion that are less than 0.15 or greater than 0.85, these confidence
## intervals may not perform well.
### R output:
# Warning message:
# In sens_plot(method = "parametric", type = "line", q = 0.1823216, :
# Calculating parametric confidence intervals in the plot. For values of Phat that are less than 0.15 or greater than 0.85, these confidence intervals may not perform well.
### Website output:
knitr::include_graphics("mytest-expected/014.png")
test12 kodama_prepped.csv - correct
# MM did this one
d = read.csv("Datasets for website test/kodama_prepped.csv")
confounded_meta(method="calibrated",
q=log(1.5),
r=0.3,
tail="below",
muB=log(1.5),
dat = d,
yi.name = "yi",
vi.name = "vi")
## Warning in norm.inter(t, adj.alpha): extreme order statistics used as endpoints
## Value Est SE CI.lo CI.hi
## 1 Prop 0.937500 0.10273108 0 1.000000
## 2 Tmin 1.003351 0.02520898 1 1.130696
## 3 Gmin 1.061336 0.12008544 1 1.515113
### R output:
# Value Est SE CI.lo CI.hi
# 1 Prop 0.937500 0.09653012 0.4375 1.000000
# 2 Tmin 1.003351 0.02353298 1.0000 1.119580
# 3 Gmin 1.061336 0.11581616 1.0000 1.485475
# Warning message:
# In norm.inter(t, adj.alpha) : extreme order statistics used as endpoints
### Website output:
knitr::include_graphics("mytest-expected/015.png")
